Parks can effectively protect ungulates and elephants despite a constant poaching threat
Bibliographic record
Abstract
Establishing protected areas is a key element of most conservation strategies, but continuous effective management is needed to ensure that biodiversity is protected. Here we provide an evaluation of park effectiveness by quantifying changes in the abundance of the commonly occurring ungulates (bushbuck - Tragelaphus scriptus; red duiker - Cephalophus harveyi; blue duiker - Cephalophus moniticola, bushpigs - Potomochoerus larvatus, and giant forest hogs - Hylochoerus meinertzhageni), and African elephants (forest elephants - Loxodonta cyclotis, savanna elephants - Loxodonta africana, and their hybrids) in Kibale National Park, Uganda and consider the financial commitment needed to ensure protection. Following the upgrading of the protection of Kibale from a forest reserve to a national park, wildlife populations generally increased. Bushpigs are an exception and their populations appear stable. The increases of ungulate and elephant populations typically happened soon after park establishment, with the exception of the giant forest hog that only recently showed an increase. We conducted an assessment of Uganda Wildlife Authorities budget and those of the small NGOs working in the area and considered the contribution that Makerere University Biological Field Stations makes to the community. This analysis indicates that with a small budget UWA and its collaborators have been successful in protecting the park’s ungulate and elephant populations. While the past decades have demonstrated the park’s ability to conserve biodiversity with limited resources, the changing climate and mounting pressures faced by the park will necessitate significantly increased investments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".